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https://github.com/KennethanCeyer/awesome-numpy
Awesome list of NumPy
https://github.com/KennethanCeyer/awesome-numpy
List: awesome-numpy
atlas awesome awesome-list blas cupy data-science lapack numba numpy numpy-library numpy-tutorial python python-package scipy
Last synced: 16 days ago
JSON representation
Awesome list of NumPy
- Host: GitHub
- URL: https://github.com/KennethanCeyer/awesome-numpy
- Owner: KennethanCeyer
- License: cc0-1.0
- Created: 2021-03-08T04:15:11.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-07T11:05:03.000Z (almost 2 years ago)
- Last Synced: 2024-04-13T23:10:35.655Z (8 months ago)
- Topics: atlas, awesome, awesome-list, blas, cupy, data-science, lapack, numba, numpy, numpy-library, numpy-tutorial, python, python-package, scipy
- Homepage: https://github.com/KennethanCeyer/awesome-numpy
- Size: 42 KB
- Stars: 8
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-numpy - Awesome list of NumPy. (Other Lists / Monkey C Lists)
README
# Awesome NumPy [](https://github.com/sindresorhus/awesome)
Awesome list of NumPy
## Contents
- [Ecosystem](#ecosystem)
- [Core](#core)
- [Hardware acceleration](#hardware-acceleration)
- [Jit Compiler](#jit-compiler)
- [AI/ML](#aiml)
- [Community](#community)
- [Conference](#conference)
- [Channels](#channels)
- [Roadmap & Milestones](#roadmap-milestones)
- [Materials](#materials)
- [Books](#books)
- [Videos](#videos)
- [Reading Materials](#reading-materials)
## Ecosystem
### Core
- [NumPy](https://numpy.org/) - Main project of NumPy.
- [SciPy](https://scipy.org/) - Extends NumPy for data science purpose.
#### Core dependencies
- [ATLAS](http://math-atlas.sourceforge.net/) - Automatically Tuned Linear Algebra Software.
- [BLAS](http://www.netlib.org/blas/) - Basic Linear Algebra Subprograms.
- [LAPACK](http://www.netlib.org/lapack/) - Linera Algebra Package.
- [FFT](https://www.fftw.org/) - library for efficient computation of the discrete Fourier transform.
### Extended library
- [Blaze](https://blaze.readthedocs.io/en/latest/index.html) - A high-level library, It provides compatible interface with NumPy.
- [Numexpr](https://github.com/pydata/numexpr) - Fast numerical expression evaluator for NumPy.
### Hardware acceleration
- [CuPy](https://github.com/cupy/cupy) - NumPy-compatible library for GPU-accelerated computing.
### Distributed Computing
- [Dask](https://dask.org/) - Distributed computing platform for NumPy and Pandas.
### JIT Compiler
- [Numba](https://numba.pydata.org/) - JIT compiler that translates a subset of NumPy.
### AI/ML
- [JAX](https://github.com/google/jax) - Extends NumPy by ML purpose, [Autograd](https://github.com/hips/autograd) and [XLA](https://www.tensorflow.org/xla) project.
- [AutoGrad](https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code.
- [TensorFlow NumPy](https://www.tensorflow.org/guide/tf_numpy) - A subset of the NumPy API implemented in TensorFlow.
- [NumPy ML](https://github.com/ddbourgin/numpy-ml) - Collection of ML models, algorithms, & tools using NumPy and Python standard lib.
## Community
### Conference
- [SciPy US](http://conference.scipy.org/)
- [EuroSciPy](https://www.euroscipy.org/)
- [SciPy India](https://scipy.in/2021)
- [PyData](https://pydata.org/event-schedule/)
- [PyCon](https://pycon.org/)
- [O'Reilly AI Conference](https://www.oreilly.com/conferences/)
### Channels
- [NumPy Slack](https://numpy-team.slack.com/)
### Roadmap & Milestones
- [The Scope of NumPy](https://numpy.org/neps/scope.html)
- [Roadmap](https://numpy.org/neps/)
- [Wish List](https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22)
## Materials
### Books
- [Guide to NumPy - Travis E. Oliphant, PhD](http://web.mit.edu/dvp/Public/numpybook.pdf)
- [From Python to Numpy - Nicolas P. Rougier](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)
- [Elegant SciPy - Juan Nunez-Iglesias](https://books.google.com/books?id=4tOdjgEACAAJ)
- [Numerical Python - Robert Johansson](https://books.google.com/books?id=N4-dvAEACAAJ)
### Videos
- [Inside NumPy - Barkeley Institute for Data Science (BIDS)](https://www.youtube.com/watch?v=fowHwlpGb34)
### Reading Materials
- [NumPy Cheat Sheet: Data Analysis in Python](https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python)
- [Faster Python calculations with Numba: 2 lines of code, 13× speed-up](https://pythonspeed.com/articles/numba-faster-python/)